Image processing apparatus, method of controlling the same, and storage medium

ABSTRACT

An image processing apparatus and a method of controlling the image processing apparatus are provided. The image processing apparatus holds a type of image data and a setting item of image processing corresponding to the type in association with each other, and obtains, by applying a learned learning model to image data which are input, a result of classifying the image data into the type. The image processing apparatus presents, based on the setting item input by a user and the held setting item that corresponds to the type of the obtained image, a recommended setting item corresponding to the image data to the user.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to an image processing apparatus, a methodof controlling the same, and a storage medium.

Description of the Related Art

There is an existing Multi Function Peripheral (hereinafter, MFP) thatreads an image of an original document and performs printing based ongenerated image data or transmission of the generated image data. Whengenerating image data by the MFP as described above, a user can changesetting items that are displayed on a user interface (hereinafter, UI)installed in the MFP to perform printing or transmission. Examples ofthe setting item include such a setting item for image processing thatchanges brightness or color tone.

In Japanese Patent Laid-Open No. 2008-282204, in accordance with ananalysis result of image data to be subjected to image processing, a UIfor changing a selected setting item is displayed. However, in a casewhere the user has poor operational experience of the MFP, or in a casewhere the user does not have knowledge of the image processing, sincethe setting items displayed on the UI cannot be appropriately set, thereis a problem in that the user cannot obtain a desired image data.

SUMMARY OF THE INVENTION

An aspect of the present invention is to eliminate the above-mentionedproblem with conventional technology.

A feature of the present invention is to provide a technique that canreduce a load on a user to set a setting item, and that can provide aprinted matter desired by a user even for a user with poor knowledge ofimage processing.

According to a first aspect of the present invention, there is providedan image processing apparatus comprising: at least one processor and atleast one memory configured to function as: a holding unit that holds atype of image data and a setting item of image processing correspondingto the type in association with each other; an operation unit that isoperable to cause a user to input the setting item; an obtaining unitthat obtains, by applying a learned learning model to image data whichare input, a result of classifying the image data into the type; and apresentation unit that presents, based on the setting item input by theoperation unit and the setting item that corresponds to the type of theimage data obtained by the obtaining unit and is held by the holdingunit, a recommended setting item corresponding to the image data to theuser.

According to a second aspect of the present invention, there is provideda method of controlling an image processing apparatus which holds a typeof image data and a setting item of image processing corresponding tothe type in association with each other, the method comprising: causinga user to input the setting item; obtaining, by applying a learnedlearning model to image data which is input, a result of classifying theimage data into the type; and presenting, based on the setting item thatis input and the setting item that corresponds to the type of the imagedata that is obtained, and that is held, a recommended setting itemcorresponding to the image data to the user.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention and,together with the description, serve to explain the principles of theinvention.

FIG. 1 depicts a schematic view illustrating a configuration of an imageprocessing system according to a first exemplary embodiment of thepresent invention.

FIG. 2 is a block diagram for explaining a hardware configuration of anMFP according to the first exemplary embodiment.

FIG. 3 is a diagram illustrating an example of a console unit of the MFPaccording to the first exemplary embodiment and a home screen displayedthereon.

FIG. 4 is a diagram illustrating an example of a scan operation screendisplayed on the console unit by a user pressing a “scan” button at theconsole unit of the MFP according to the first exemplary embodiment.

FIGS. 5A to 5D are diagrams illustrating screen examples displayed onthe console unit of the MFP according to the first exemplary embodiment.

FIG. 6 is a flowchart for explaining processing of scanning a paperdocument and obtaining scanned image data, in the MFP according to thefirst exemplary embodiment.

FIGS. 7A and 7B are diagrams illustrating an example in whichclassification processing of the scanned image data in step S605 isperformed by using a learned model.

FIG. 8 is a diagram illustrating a configuration example of a neuralnetwork in an image classification unit according to the learned model.

FIG. 9 is a diagram for explaining a learning image and correct answerdata according to the first exemplary embodiment.

FIGS. 10A to 10C are diagrams for explaining types of the scanned imagedata and a change item of a scan setting corresponding thereto in theMFP according to the first exemplary embodiment.

FIG. 11 is a diagram illustrating an example of a suggestion screen tobe presented to a user in step S608 in the MFP according to the firstexemplary embodiment.

FIGS. 12A to 12E are diagrams illustrating operation screen examples atthe time of copy operation displayed on an MFP according to a secondexemplary embodiment.

FIG. 13 is a flowchart for explaining processing of scanning a paperdocument and obtaining print image data in the MFP according to thesecond exemplary embodiment.

FIGS. 14A and 14B are diagrams for explaining types of the scanned imagedata and a change item of a copy setting corresponding thereto in theMFP according to the second exemplary embodiment.

FIG. 15 is a diagram illustrating an example of a suggestion screen tobe presented to a user in the MFP according to the second exemplaryembodiment.

FIG. 16 is a flowchart for explaining processing of scanning pluralpieces of paper documents and obtaining scan image data in an MFPaccording to a third exemplary embodiment.

FIGS. 17A and 17B are diagrams illustrating examples of suggestionscreens to be presented to a user in the MFP according to the thirdexemplary embodiment.

FIG. 18 is a flowchart for explaining processing of scanning pluralpieces of paper documents and obtaining scanned image data in an MFPaccording to a fourth exemplary embodiment.

FIG. 19 is a diagram illustrating an example of a suggestion screen tobe presented to a user in the MFP according to the fourth exemplaryembodiment.

DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present invention will be described hereinafter indetail, with reference to the accompanying drawings. It is to beunderstood that the following embodiments are not intended to limit theclaims of the present invention, and that not all of the combinations ofthe aspects that are described according to the following embodimentsare necessarily required with respect to the means to solve the problemsaccording to the present invention.

In embodiments described below, in a case where a user uses an MFP toscan a paper document, using the MFP that creates an electronic documentusing image data (scanned image data) obtained by the scanning as anexample, an example of an image processing apparatus according to thepresent invention will be described.

First Exemplary Embodiment

FIG. 1 depicts a schematic view illustrating a configuration of an imageprocessing system according to a first exemplary embodiment of thepresent invention.

In this image processing system, a multi-function peripheral(hereinafter, MFP) 101 and a computer (hereinafter, PC) 102 areconnected via a network 103. In the diagram, dotted lines 104 and 105indicate the flow of processing, and the dotted line 104 indicatesprocessing in which a user uses a scanner of the MFP 101 and causes itto read a paper document. At this time, the user operates a console unit(203 in FIG. 2) of the MFP 101 to be described later, and can performvarious settings related to a destination (for instance, the PC 102) towhich scanned image data is transmitted, scanning, and transmission. Asthe various settings, the user can designate a resolution, a compressionratio, a data format (for instance, JPEG, TIFF, PDF, color compressionof small number of colors, color compression of small number of colors(with OCR result)), and the like. The dotted line 105 indicatesprocessing for, based on the designated various settings, generatingimage data by using a software or hardware function of the MFP 101 andtransmitting the image data to the designated destination. Here, theimage data transmitted to the PC 102 is transmitted in a file formatsuch as PDF, and thus can be viewed with a general-purpose viewerincluded in the PC 102.

FIG. 2 is a block diagram for explaining a hardware configuration of theMFP 101 according to the first exemplary embodiment.

The MFP 101 has a scanner 201 which is an image input device, and aprinter 202 which is an image output device, a control unit 204 having amemory and the like, a console unit 203 which is a user interface, acard reader 220 which performs user authentication for using the MFP,and the like. The control unit 204 is a controller for performinginput/output of image information and device information by connectingto the scanner 201, the printer 202, and the console unit 203 whilesimultaneously connecting to a LAN 209. A CPU 205 is a processor thatcontrols the system as a whole. A RAM 206 provides a system work memoryfor operation of the CPU 205, and also provides an image memory fortemporarily storing image data. A ROM 210 is a boot ROM, and programssuch as a boot program of the system are stored. A storage 211 isconstituted of, for instance, a hard disk drive or the like, and storessystem control software, image data, and the like. A console unit I/F207 is an interface unit with the console unit (UI) 203, and outputsimage data to be displayed on the console unit 203 to the console unit203. Furthermore, the console unit I/F 207 plays a role of transmittinginformation input by the user from the console unit 203 to the CPU 205.A network I/F 208 connects the MFP 101 to the LAN 209 and performsinput/output of packet form information through the LAN 209. A cardreader I/F 221 is an interface unit with the card reader 220, and playsa role of transmitting information read by the card reader 220 to anauthentication information management unit 222. The devices describedabove are arranged on a system bus 216. An image bus interface 212 is abus bridge that connects the system bus 216 and an image bus 217 thattransfers image data at high speed to each other and converts the datastructure. The image bus 217 is constituted of, for instance, a PCI busor IEEE 1394.

Devices as described below are arranged on the image bus 217. A rasterimage processor (RIP) unit 213 achieves so-called rendering processingthat analyzes a page description language (PDL) code and deploys into abitmap image of a designated resolution. A device I/F 214 connects tothe scanner 201 through a signal line 218 and connects to the printer202 through a signal line 219, and performs synchronous/asynchronousconversion of image data. A data processing unit 215 performs, at thetime of scanning operation, on scanned data input from the scanner 201,image processing, and processing such as JPEG compression or OCR. Withthis, scanned image data are generated. Furthermore, the data processingunit 215 performs, at the time of printing operation, image processingof print image data that is output to the printer 202. The scanned imagedata generated at the time of scanning operation is transmitted throughthe network I/F 208 and the LAN 209 to the designated destination (forinstance, the client PC 102). Furthermore, the data processing unit 215can also perform decompression of the compressed data received throughthe network I/F 208 and the LAN 209. The decompressed image data istransmitted to the printer 202 through the device I/F 214 and printingis performed. A correspondence table management unit 223 manages acorrespondence table 1001 that stores a classification result of alearned model (learning model) of FIGS. 10A-10C, described later, and arecommended setting corresponding thereto in association with eachother. Note that this management may be performed by being associatedwith an authenticated user.

FIG. 3 is a diagram illustrating an example of the console unit 203 ofthe MFP 101 according to the first exemplary embodiment and a homescreen displayed thereon.

A window 301 displays application selection buttons each of which callsan appropriate operation screen in accordance with a user instruction.In FIG. 3, a “copy” button 302, a “scan” button 303, a “fax” button 304,and a “box” button 305 are displayed that respectively call operationscreens of a copy function, a network scanning function, a fax function,and a box function. Furthermore, on the console unit 203, a numerickeypad 306, and a start key 307 for instructing start of a job arearranged. In addition, a “setting registration” button 308 for calling asetting registration screen of the apparatus and a “history status”button 309 for calling a job history status screen are also arranged.

FIG. 4 is a diagram illustrating an example of a scan operation screendisplayed on the console unit 203 by a user pressing the “scan” button303 at the console unit 203 of the MFP 101 according to the firstexemplary embodiment.

In this diagram, a user can designate a destination by pressing onedestination input button among “address book”, “one-touch”, and “newdestination” of a region 401. Furthermore, a “transmit email to self”button 402 for setting an e-mail address tied to a user is alsodisplayed. A transmission settings button 403 is used to settransmission settings such as a resolution, a color mode, size, filedesignation of scanned data. An advanced functions button 404 is abutton for calling various detailed settings at the time of reading andtransmission.

FIGS. 5A to 5D are diagrams illustrating screen examples displayed onthe console unit 203 of the MFP 101 according to the first exemplaryembodiment.

FIG. 5A illustrates an example of an advanced functions screen 501displayed on the console unit 203 by a user pressing the “advancedfunctions” button 404. On the advanced functions screen 501, in thefirst exemplary embodiment, a “type of original” button 502, a “densityadjustment” button 503, and a “sharpness” button 504 are displayed.Here, when the user presses the “type of original” button 502, a “typeof original” setting change screen in FIG. 5B is displayed on theconsole unit 203.

On this “type of original” setting change screen, “character”,“photograph”, and “character/photograph” buttons are displayed. In thefirst exemplary embodiment, when a “character” button 505 is selected,such filtering processing where the sharpness of the whole image isimproved is applied to the scanned image data. Furthermore, when the“photograph” button is selected, such filtering processing where thesharpness of the whole image is deteriorated is applied to the scannedimage data. Furthermore, when the “character/photograph” button isselected, filtering processing where the sharpness of the whole image isslightly improved is applied to the scanned image data. The user selectsany one of the buttons and presses an OK button 507, whereby a settingchange is applied. Furthermore, by pressing a cancel button 506, it ispossible to cancel the setting change by this screen.

Furthermore, on the screen in FIG. 5A, when the user presses the“density adjustment” button 503, a “density adjustment/backgroundadjustment” setting change screen in FIG. 5C is displayed on the consoleunit 203.

On this “density adjustment/background adjustment” setting changescreen, a density adjustment slider 508 and a background adjustmentslider 509 are displayed. For instance, when the density adjustmentslider 508 is moved in the leftward direction, the processed image isbrightened, and when the slider 508 is moved in the rightward direction,the processed image is darkened. Furthermore, for instance, when thebackground adjustment slider 509 is moved in the leftward direction, thebackground color of a paper document is emphasized in the processedimage, and when the slider 509 is moved in the rightward direction, thebackground color of the paper document is processed to a highlight sidein the processed image. Furthermore, the cancel button 506 and the OKbutton 507 displayed on the “density adjustment/background adjustment”setting change screen respectively have functions similar to the buttonsdisplayed on the screen in FIG. 5B.

Furthermore, on the screen in FIG. 5A, when the user presses the“sharpness” button 504, a “sharpness” setting change screen in FIG. 5Dis displayed on the console unit 203.

On the “sharpness” setting change screen, a sharpness adjustment slider510 is displayed, and for instance, when the slider 510 is moved in theleftward direction, the sharpness of the processed image is lowered, andwhen the slider 510 is moved in the rightward direction, the sharpnessof the processed image is heightened. Furthermore, the “cancel” button506 and the “OK” button 507 displayed on the “sharpness” setting changescreen respectively have functions similar to the buttons displayed onthe screen in FIG. 5B.

FIG. 6 is a flowchart for explaining processing of scanning a paperdocument and obtaining scanned image data, in the MFP 101 according tothe first exemplary embodiment. Note that the processing described inthis flowchart is achieved by the CPU 205 deploying the control programstored in the storage 211 to the RAM 206 and executing the program.

This processing is started by the “scan” button 303 being pressed on thescreen in FIG. 3. First, in step S601, a user holds a card for userauthentication over the card reader 220 or inputs a user name andpassword on a user authentication screen displayed on the console unit203, whereby the CPU 205 performs user authentication by using theauthentication information management unit 222. Note that this userauthentication may be performed by the CPU 205 executing a programwithout using the authentication information management unit 222. Next,the process proceeds to step S602, the CPU 205 determines whether or notthe user authentication is successful. Here, in a case where theauthentication is successful, the process proceeds to step S603,otherwise this process is terminated.

In step S603, for instance, the CPU 205 displays the scan operationscreen in FIG. 4, and further accepts input of a scan setting and adestination input via the advanced functions screens in FIGS. 5A to 5D.Then, when the start key 307 arranged on the console unit 203 ispressed, the CPU 205 starts a scan job. Then, the process proceeds tostep S604, when the scan job is started, the CPU 205 controls thescanner 201 to scan a paper document, and scanned image data isdelivered to the data processing unit 215 through the device I/F 214 andthe image bus 217.

Next, the process proceeds to step S605, the CPU 205 uses the dataprocessing unit 215 and performs classification on the scanned imagedata by using a learned model.

FIGS. 7A and 7B are diagrams illustrating an example in whichclassification processing of the scanned image data in step S605 isperformed by using a learned model.

Furthermore, the description is given by assuming that the CPU 205executes the program deployed to the RAM 206 and achieves each processdescribed below. The description is given by assuming that, while alsoappropriately saving to the RAM 206 and loading from the RAM 206 thescanned image data obtained by the scanner 201 and intermediategeneration data of each process, the CPU 205 performs computationprocessing.

In FIG. 7A, in the first exemplary embodiment, a classification imagegeneration unit 701 using scanned image data receives scanned image dataobtained by the scanner 201 and expressed by RGB. Next, theclassification image generation unit 701 uses the received image dataand generates image data for being input to an image classification unit702 according to a learned model to be described later. For instance, ina case where the input image of the image classification unit 702according to the learned model is RGB image data with size of 256×256,the scanned image data is reduced in accordance with the input imagesize. Note that the reduction method uses a known technology.

Hereinafter, the configuration of the image classification unit 702according to the learned model will be described.

FIG. 8 is a diagram illustrating a configuration example of a neuralnetwork in the image classification unit 702 according to the learnedmodel. The operation of the first exemplary embodiment will be describedbelow based on the neural network illustrated in FIG. 8, but thetechnology according to the present invention is not limited thereto.For instance, a neural network having a deeper layer may be used, or aform of U-net may be used. In addition, in the first exemplaryembodiment, RGB image data of 256×256 is used as input data, but thepresent invention is not limited thereto.

First, in a Convolution Layer 801, a convolution operation is performedon the input image data input to the image classification unit 702according to the learned model. When the pixel value of an (x, y)position of the input image data is assumed to be I(x, y), output imagedata IG1(x, y, p) of the Convolution Layer 801 is calculated by Equationdescribed below.

IG1(x,y,p)=Σ_(s=−1) ^(s=1)Σ_(t=−1) ^(t=1)Σ_(z=1) ^(z=3) Wstpz ^(G1)I(x+s,y+t,z)  (1)

Here, p is the number of output planes, and p=2 is used in the firstexemplary embodiment. Note that the number of output planes is merely anexample and is not limited thereto.

In addition, Wstpz^(G1) is a weight in the Convolution Layer 801 held bythe neural network, and has a different value for each combination of s,t, and p. Note that, in I(x+s, y+t) in the above Equation, in a casewhere a reference location is outside a pixel location of the inputimage data (for instance, I(−1, −1), or the like), the computation isperformed by taking the pixel value as “0”. By the above computation,the output of the Convolution Layer 801 is image data of 256×256×2.

Next, in an Activation Layer 802, a non-linear function is applied tothe output image data IG1(x, y, p) of the Convolution Layer 801. Morespecifically, output image data IG2(x, y, p) of the Activation Layer 802is calculated by using a ramp function by Equation (2) descried below.

IG2(x,y,p)=max(0,IG1(x,y,p))  Equation (2)

Note that the non-linear function applied in the processing is notlimited thereto. For instance, a hyperbolic tangent function and thelike may be used. By the above computation, the output image data IG2(x,y, p) of the Activation Layer 802 becomes image data of 256×256×2, thatis, image data including two planes of 256×256 image data.

Next, compression of information is performed in a Pooling Layer 803.Here, by performing 2×2 max pooling, reduction of the output image dataIG2(x, y, p) is performed. More specifically, output image data IG3(u,v, p) of the Pooling Layer 803 is calculated by Equation (3) describedbelow.

IG3(u,v,p)=max(IG2(2 u,2 v,p), IG2(2 u,2 v+1,p), IG2(2 u+1,2v,p), IG2(2u+1,2v+1,p))  Equation (3)

Note that the ranges of u and v are 0≤u≤127, 0≤v≤127, respectively. Bythe above computation, the output image data IG3(u, v, p) of the PoolingLayer 803 becomes image data of 128×128×2.

Next, a Full Connected Layer 804 calculates a matrix A of 1×8 from theoutput image data IG3(u, v, p) of the Pooling Layer 803. The matrix Aoutput by the Full Connected Layer 804 is calculated by Equation (4)described below.

A=Σ _(u=1) ^(u=127)Σ_(v=1) ^(v=127)Σ_(p=1) ^(p=2) Wuvp ^(G4)IG3(u,v,p)  Equation (4)

Note that Wuvp^(G4) is here a weight held by the neural network.

Finally, an Activation Layer 805 applies non-linear processing to the“a” value and outputs the determination result. Here, by applying asoftmax function,

$\begin{matrix}{{S({Ai})}{= \frac{e^{Ai}}{\sum_{n = 1}^{n = 8}e^{Ai}}}} & {{Equation}\mspace{14mu} (5)}\end{matrix}$

It is possible to obtain such a 1×8 matrix S (806) that each element inmatrix A is made to be in a value range [0, 1] and the sum of therespective elements becomes “1”.

Here, i may take a value from 1 to 8.

Next, a technique for obtaining a classification result of an imageusing the estimated value matrix 806 will be described with reference toFIG. 7B.

For instance, as the result of inputting scanned image data formed onlyof a document such as a document image 703 as an input image of thelearned model in FIG. 8, it is assumed that the values of the respectiveelements of the estimation matrix 806 are respectively obtained asvalues indicated by a reference numeral 704. In this case, a firstarrangement 705 has the maximum value. Next, in a correspondence table706 of the arrangement and the image type, a type corresponding to thefirst arrangement having the maximum value is a “document image” 707. Inthis manner, as the classification result of the scanned image data, the“document image” is obtained.

This concludes the explanation of the image classification unit 702according to the learned model in FIG. 7A.

Next, training data and correct answer data according to the firstexemplary embodiment will be described with reference to FIG. 9. Thetraining data in the first exemplary embodiment is generated from imagedata printed on a recording medium (paper or the like).

FIG. 9 is a diagram explaining a training image and the correct answerdata according to the first exemplary embodiment.

A reference numeral 901 indicates, as a result of visually classifyingan arbitrary image group beforehand into eight types of a “documentimage”, a “photograph”, a “recycled paper document”, a “license”, a“drawing”, a “presentation material”, an “answer sheet”, and a“receipt”, an image group determined to be the document image.Furthermore, reference numerals 903, 905, 907, 909, 911, 913, and 915each also indicate an image group that is classified in the same manner.Additionally, matrices 902, 904, 906, 908, 910, 912, 914, and 916 areeach constituted of a matrix of 1×8. In the matrix 902, the value of thefirst arrangement is set to “1” and the values of the other arrangementsare each set to “0”. In the same manner, the values of the secondarrangement of the matrix 904, the third arrangement of the matrix 906,the fourth arrangement of the matrix 908, the fifth arrangement of thematrix 910, the sixth arrangement of the matrix 912, the seventharrangement of the matrix 914, and the eighth arrangement of the matrix916 are set to “1”, and the values of the other arrangements of thematrices are all set to “0”.

Here, by using the document image 901 as the training data and thematrix 902 as the correct answer data, the neural network illustrated inFIG. 8 is made to learn. Similarly, also by using the image 903 and thematrix 904, the image 905 and the matrix 906, the image 907 and thematrix 908, the image 909 and the matrix 910, the image 911 and thematrix 912, the image 913 and the matrix 914, and the image 915 and thematrix 916, the learning is performed.

Then, returning to FIG. 6, in step S606, based on the classified type ofthe scanned image data and the correspondence table of recommendedsetting to be described later, the CPU 205 determines whether or not asetting item input by a user is appropriate. This determination methodwill be described with reference to FIGS. 10A to 10C.

FIGS. 10A to 10C are diagrams for explaining types of the scanned imagedata and a scan setting item corresponding thereto in the MFP 101according to the first exemplary embodiment.

FIG. 10A illustrates the correspondence table 1001 representing aclassification result of the learned model and a recommended settingcorresponding thereto. Here, for instance, in a case where the scannedimage data is classified by the learned model into the “document image”,the setting items described in the “recommended setting” of thecorrespondence table 1001 are “density adjustment+1”, “character mode”,and “300×300 dpi”. These setting items are items that can be set andchanged by user operation via the scan operation screen in FIG. 4 andthe advanced functions screen in FIGS. 5A to 5D. Then, in step S606, theCPU 205 determines whether or not this recommended setting and thesetting item input via the console unit 203 by the user match with eachother. Here, in a case where it is determined that the two match witheach other, it is determined that the setting item input by the user isappropriate, and the process proceeds to step S607, but otherwiseproceeds to step S608. In step S607, the CPU 205 executes imageprocessing based on the setting item input by the user through theconsole unit 203 at the data processing unit 215, and the process isterminated. A method for generating scanned data based on the settingitem here will be described with reference to FIG. 10B.

For instance, it is assumed that, for a document image 1002, the userselects “300×300 dpi” in the transmission settings button 403 in FIG. 4,selects the “character” button 505 in FIG. 5B, and moves the densityadjustment slider 508 in FIG. 5C by one stage in the rightwarddirection. In this case, for instance, the scanner 201 first scans thepaper document at 300×300 dpi and creates scanned image data. Next,since the “character” button 505 is selected, filtering processing forimproving sharpness is applied to the whole image. Next, since thedensity adjustment slider 508 is moved by one stage in the rightwarddirection, for instance, the RGB signal value of the whole image isreduced by “10”. By the processing described above, scanned image data1003 is generated.

On the other hand, in a case where the setting item input by the user isnot appropriate, the process proceeds to step S608. In step S608, theCPU 205 presents, to the console unit 203, a suggestion whether or notto execute image processing with the recommended setting based on theclassification result of the learned model and the correspondence table1001 representing the recommended setting corresponding theretoillustrated in FIG. 10A.

FIG. 11 is a diagram illustrating an example of a suggestion screen 1101to be presented to the user in step S608 in the MFP 101 according to thefirst exemplary embodiment.

Here, a recommended setting item 1102 is displayed based on the resultof classifying the scanned image data with the learned model in FIG. 8and the correspondence table 1001 in FIG. 10A. Note that the suggestionscreen 1101 is not limited thereto, and for instance, an image ofscanned image data to be obtained by executing image processing with therecommended setting, a thumbnail image, for instance, or the like may bedisplayed on the suggestion screen 1101.

Then, the process proceeds to step S609, the CPU 205 determines whetheror not a “perform file generation with recommended setting” button 1103is pressed on this suggestion screen 1101. Here, in a case where it isdetermined that the “perform file generation with recommended setting”button 1103 is pressed, the process proceeds to step S610, and imageprocessing based on the recommended setting item is executed, and theprocess is terminated. For instance, in a case where the result ofclassifying the scanned image with the learned model is the “documentimage” in FIG. 10A, generation of scanned data to which “densityadjustment+1”, “character”, “300×300 dpi”, which constitute acombination of the setting items corresponding thereto, are applied isexecuted. The details of this processing are similar to the processingdescribed in step S607.

On the other hand, in step S609, in a case where the CPU 205 determinesthat a “perform file generation with input setting” button 1104 ispressed, the process proceeds to step S611. In step S611, the CPU 205updates the correspondence table 1001 in FIG. 10A based on the settingitem input by the user and the classification group when scanned imagedata are classified with the learned model.

A specific example is described with reference to FIG. 10C. Forinstance, in a case where the result of classifying the scanned imagedata with the learned model is the “document image” in FIG. 10A, arecommended setting corresponding thereto is indicated by 1004. Then,the recommended setting 1004 corresponding to this “document image” isupdated as in a recommended item 1006 based on a setting item 1005 inputby the user. The correspondence table 1001 updated in this manner may bemanaged by the correspondence table management unit 223 per-user basis.Note that the processing for updating is not limited to this example.For instance, the number of times the “perform file generation withinput setting” button 1104 is pressed may be managed per-user basis,stored in the correspondence table management unit 223, and updated forthe first time when the number of times reaches a predetermined numberof times which is determined beforehand. Note that the correspondencetable 1001 may be switched per-user basis in accordance with informationfrom the authentication information management unit 222.

Subsequently, the process proceeds to step S612, in the same manner asin step S607, the CPU 205 executes generation of scanned data based onthe setting item input by the user through the console unit 203 at thedata processing unit 215, and the process is terminated. The details ofthe processing of generation of the scanned data based on the settingitem here are similar to the processing described in step S607.

As described above, according to the first exemplary embodiment, theload on the user to change the setting of the image processing for theinput image data can be reduced. Furthermore, even when the user haspoor knowledge of the image processing, it is possible to providescanned image data desired by the user. Further it is possible toprovide a printed matter using the scanned image data.

Second Exemplary Embodiment

Next, as a second exemplary embodiment, a configuration for generatingprint image data at the time of copy operation will be described. Notethat a hardware configuration and the like of the MFP 101 according tothe second exemplary embodiment is the same as that of the firstexemplary embodiment described above, and thus the descriptions thereofwill be omitted.

FIGS. 12A to 12E are diagrams illustrating operation screen examples atthe time of copy operation displayed on the MFP 101 according to thesecond exemplary embodiment.

FIG. 12A illustrates an example of a copy operation screen 1201displayed on the console unit 203 when the “copy” button 302 is pressedon the home screen of FIG. 3. In this diagram, a “color selection”button 1202 selects whether to generate print image data in an RGB imageor a gray-scale image. A “scaling” button 1203 changes an enlargementmagnification and a reduction magnification of the print image data. A“density adjustment” button 1204 has a function equivalent to the“density adjustment” button 503 in FIG. 5A of the first exemplaryembodiment, and when the user presses it, a “density adjustment” settingchange screen in FIG. 12D is displayed. A density adjustment slider 1218in FIG. 12D has the same function as the density adjustment slider 508of FIG. 5C. Furthermore, a background adjustment slider 1219 performsthe same processing as the background adjustment slider 509 in FIG. 5C.Furthermore, a cancel button 1216 and an OK button 1217 in FIGS. 12C to12E have the same functions as the OK button 507 and the cancel button506 displayed on the “type of original” setting change screen in FIG.5B.

A “type of original” button 1205 has a function equivalent to the “typeof original” button 502 in FIG. 5A of the first exemplary embodiment.When the “type of original” button 1205 is pressed, the “type oforiginal” setting change screen in FIG. 5B is displayed, and“character”, “photograph”, and “character/photograph” buttons aredisplayed on this screen. Furthermore, image processing applied toscanned image data when each button is selected is the same as thatdescribed in FIGS. 5A to 5D. Furthermore, when an “advanced functions”button 1206 is pressed, an advanced functions screen 1208 in FIG. 12B isdisplayed on the console unit 203. This advanced functions screen 1208will be described later. In addition, on a number of copies displayportion 1207, when the user inputs the number of copies of a printedmatter by using the numeric keypad 306 arranged on the console unit 203in FIG. 3, the number of copies is displayed.

On the advanced functions screen 1208 in FIG. 12B, a “color adjustment”button 1209 and a “sharpness” button 1210 are displayed. When the userpresses the “color adjustment” button 1209, a “color adjustment” settingchange screen 1211 in FIG. 12C is displayed on the console unit 203. Onthe “color adjustment” setting change screen 1211, a yellow adjustmentslider 1212, a magenta adjustment slider 1213, a cyan adjustment slider1214, and a black adjustment slider 1215 are displayed. For instance,when the yellow adjustment slider 1212 is moved in the leftwarddirection, the yellow component after the image processing decreases,and when the slider 1212 is moved in the rightward direction, the yellowcomponent after image processing increases. The magenta adjustmentslider 1213, the cyan adjustment slider 1214, and the black adjustmentslider 1215 also each have the same function.

FIG. 13 is a flowchart for explaining processing of scanning a paperdocument and obtaining print image data, in the MFP 101 according to thesecond exemplary embodiment. Note that the processing described in thisflowchart is achieved by the CPU 205 deploying the control programstored in the storage 211 to the RAM 206 and executing the program. Thisprocessing is started by the “copy” button 302 being pressed on thescreen in FIG. 3. Note that in FIG. 13, processes common to those ofFIG. 6 described above are denoted by the same reference numbers, anddescriptions thereof will be omitted.

In step S1301, when input of a setting change is performed from the copyoperation screens in FIGS. 12A to 12E displayed on the console unit 203and the start key 307 arranged on the console unit 203 is pressed by auser, the CPU 205 starts a copy job. Next, the process proceeds to stepS1302, the CPU 205 controls the scanner 201 to scan a paper document,and delivers scanned image data to the data processing unit 215 throughthe device I/F 214 and the image bus 217. Then, in step S605, the dataprocessing unit 215 performs classification on the scanned image data byusing a learned model. In this classification method, the same processas that of the first exemplary embodiment is performed.

Then, the process proceeds to step S1303, based on the classified typeof the scanned image data and a correspondence table 1401 (FIG. 14A) ofrecommended setting to be described later, the CPU 205 determineswhether or not a setting item input by a user is appropriate. Thisdetermination method will be described with reference to FIGS. 14A and14B.

FIGS. 14A and 14B are diagrams for explaining types of the scanned imagedata and a change item of a copy setting corresponding thereto in theMFP 101 according to the second exemplary embodiment.

FIG. 14A illustrates the correspondence table 1401 representing aclassification result of the learned model and a recommended settingcorresponding thereto. For instance, in a case where the scanned imagedata is classified into the “document image” by the learned model, thesetting items described in the “recommended setting” of thecorrespondence table 1401 are “density adjustment+1” and “character”.These setting items are items that can be set and changed by the useroperation on the copy operation screen of FIGS. 12A to 12E. In stepS1303, the CPU 205 determines whether or not this recommended settingand the setting item input via the console unit 203 by the user matchwith each other. Here, in the case where it is determined that the twomatch with each other, it is determined that the setting item input bythe user is appropriate and the process proceeds to step S1304, and theCPU 205 executes image processing based on the setting item input by theuser through the console unit 203 at the data processing unit 215, andthe process is terminated.

A method for generating print image data based on the setting item herewill be described with reference to FIG. 14B.

For instance, it is assumed that, for the document image 1402, the userselects the “character” button 1220 in FIG. 12E and moves the densityadjustment slider 1218 in FIG. 12D by one stage in the rightwarddirection. In this case, for instance, since the “character” button isfirst selected, filtering processing for improving sharpness is appliedto the whole image. Next, since the density adjustment slider 1218 ismoved by one stage in the rightward direction, for instance, the RGBsignal value of the whole image is reduced by “10”. By the processingdescribed above, print image data 1403 is generated.

On the other hand, in step S1303, when it is determined that therecommended setting and the setting item input by the user do not matchwith each other, the process proceeds to step S1305. In step S1305, theCPU 205 displays, on the console unit 203, a suggestion whether or notto execute image processing with the recommended setting based on thecorrespondence table 1401 representing the classification result of thelearned model and the recommended setting corresponding theretoillustrated in FIG. 14A.

FIG. 15 is a diagram illustrating an example of a suggestion screen 1501to be presented to the user in the MFP 101 according to the secondexemplary embodiment.

A recommended setting item 1502 is displayed based on the result ofclassifying the scanned image data with the learned model in FIG. 8 andthe correspondence table 1401 in FIG. 14A. Note that the suggestionscreen 1501 is not limited thereto, and for instance, an image of printimage data to be obtained by executing image processing with therecommended setting or a thumbnail image may be displayed on thesuggestion screen 1501.

Next, the process proceeds to step S1306, the CPU 205 determines whetheror not a “copy with recommended setting” button 1503 is pressed on thesuggestion screen 1501. Here, when the “copy with recommended setting”button 1503 is pressed, the process proceeds to step S1307. In stepS1307, the CPU 205 executes image processing based on the recommendedsetting item, and the process is terminated. For instance, in a casewhere the result of classifying the scanned image with the learned modelis the “document image” in FIG. 14A, scanned image data to which“density adjustment+1” and “character”, which constitute a combinationof the setting items corresponding thereto, are applied are generated.The details of the processing are similar to the processing described instep S1304.

On the other hand, when a “copy with input setting” button 1504 ispressed on the suggestion screen 1501, the process proceeds to stepS1308. In step S1308, the CPU 205 updates the correspondence table 1401in FIG. 14A based on the setting item input by the user and theclassification group when scanned image data are classified with thelearned model. This update process is the same process as that in stepS611 of the first exemplary embodiment described above, and thusdescriptions thereof will be omitted.

Next, the process proceeds to step S1309, in the same manner as in stepS1304, the CPU 205 causes the data processing unit 215 to executegeneration of print image data based on the setting item input by theuser through the console unit 203, and the process is terminated. Thedetails of the processing of generation of the print image data based onthe setting item here are similar to the processing described in stepS1304, and thus descriptions thereof will be omitted.

As described above, according to the second exemplary embodiment, theload on the user to change the setting of the image processing for theinput image data can be reduced. Furthermore, in a case where the userhas poor knowledge of the image processing as well, it is possible toprovide a printed matter desired by the user in a copy function.

Third Exemplary Embodiment

In the first exemplary embodiment and the second exemplary embodimentdescribed above, the configuration in the case where a single paperdocument is scanned has been described, but in this third exemplaryembodiment, a configuration in a case where plural pieces of paperdocuments are scanned will be described. Note that in the thirdexemplary embodiment, processing for generating scanned image data willbe described, but a case where print image data at the time of copyoperation is generated can also be executed with the same configuration.Note that the hardware configuration and the like of the MFP 101according to the third exemplary embodiment is the same as that of thefirst exemplary embodiment described above, and thus the descriptionsthereof will be omitted.

FIG. 16 is a flowchart for explaining processing of scanning pluralpieces of paper documents and obtaining scan image data, in the MFP 101according to the third exemplary embodiment. Note that the processingdescribed in this flowchart is achieved by the CPU 205 deploying thecontrol program stored in the storage 211 to the RAM 206 and executingthe program. This processing is started by the “scan” button 303 beingpressed on the screen in FIG. 3. Note that in FIG. 16, processes commonto those of FIG. 6 described above are denoted by the same referencenumbers, and descriptions thereof will be omitted.

In step S1601, when a scan job is started, the CPU 205 causes thescanner 201 to scan and read the plural pieces of paper documents, and ascanned image data group is delivered to the data processing unit 215through the device I/F 214 and the image bus 217. Next, the processproceeds to step S1602, the CPU 205 performs classification, by the dataprocessing unit 215, on plural pieces of scanned image data, for eachpieces of scanned image data by using a learned model. In thisclassification method, the same process as in step S605 in the firstexemplary embodiment is performed. With this, the classification resultis determined for each piece of scanned image data corresponding to onesheet.

Next, the process proceeds to step S1603, based on the type of thescanned image data classified in this manner and the correspondencetable 1001 of recommended setting described in step S606 in FIG. 6, theCPU 205 determines whether or not the setting item input by the user isappropriate for each piece of scanned image data. Here, in all scannedimage data, in a case where the recommended setting and the setting iteminput by the user match with each other, the process proceeds to stepS1604, otherwise proceeds to step S1605. For instance, it is assumedthat all the classification results of the scanned image data groupobtained by scanning the plural pieces of paper documents with thescanner 201 are a “document image”. Then, in a case where the settingitem input by the user and the recommended setting of the “documentimage” in the correspondence table 1001 of the recommended settingdescribed in step S606 in the first exemplary embodiment match with eachother, the determination in step S1603 becomes Yes and the processproceeds to step S1604. In step S1604, the CPU 205 causes the dataprocessing unit 215 to execute, for each piece of scanned image data,generation of scanned image data based on the setting item input by theuser, and the process is terminated. The generation of the scanned databased on the setting item here is similar to step S607 in the firstexemplary embodiment.

On the other hand, in step S1603, in a case where the CPU 205 determinesthat the setting input by the user is not appropriate, the processproceeds to step S1605. In step S1605, the CPU 205 displays, on theconsole unit 203, a suggestion whether or not to execute imageprocessing with the recommended setting based on the correspondencetable 1001 representing the classification result of the learned modeland the recommended setting corresponding thereto illustrated in FIG.10A. FIG. 17 illustrates an example of the suggestion screen displayedon the console unit 203.

FIGS. 17A and 17B are diagrams illustrating examples of suggestionscreens to be presented to the user in the MFP 101 according to thethird exemplary embodiment.

For instance, it is assumed that ten sheets of paper documents arescanned, and in step S1602, the first sheet to the fifth sheet are eachclassified into a document image, and the sixth sheet to the tenth sheetare each classified into a photograph. Then, in a case where the settingchange input by the user is “character/photograph”, in step S1605, theCPU 205 displays a suggestion screen 1701 in FIG. 17A on the consoleunit 203. Here, a reference numeral 1702 indicates a page displayportion, and as a result of classification with the learned model inFIG. 8, this portion is displayed in accordance with on which sheet thescanned image group that have been determined to have the same type islocated. Furthermore, a recommended setting item 1703 is displayed basedon the result of classifying the scanned image data with the learnedmodel in FIG. 8 and the correspondence table 1001 in FIG. 10A.

Next, the process proceeds to step S1606, when the CPU 205 determinesthat a “perform file generation with recommended setting” button 1704 ispressed by the user on the screen in FIG. 17A, the process proceeds tostep S1607. In step S1607, the CPU 205 performs, for instance, imageprocessing on the scanned images of the first sheet to the fifth sheetof the scanned image data group based on the recommended setting item,and the process proceeds to step S1610. The details of this processingin step S1607 are similar to the processing described in step S610.

On the other hand, in step S1606, in a case where it is determined thatthe user presses a “perform file generation with input setting” button1705, the process proceeds to step S1608. In step S1608, the CPU 205updates the correspondence table 1001 in FIG. 10A based on the settingitem input by the user and the classification group when the scannedimage data group is classified with the learned model. For instance, inthe suggestion screen 1701 in FIG. 17A, in a case where the “performfile generation with input setting” button 1705 is pressed, therecommended setting item corresponding to the “document image” in thecorrespondence table 1001 is updated. In this update process, the sameprocess as that in step S611 of the first exemplary embodiment describedabove is performed.

Next, the process proceeds to step S1609, in the same manner as in stepS1604, the CPU 205 causes the data processing unit 215 to executegeneration of scanned image data based on the setting item input by theuser through the console unit 203, and the process proceeds to stepS1610. Here, on the scanned image data of the first sheet to the fifthsheet of the scanned image data group, based on the setting item inputby the user, the generation of scanned image data is performed. Thedetails of this processing are similar to the processing described instep S1604.

In step S1610, the CPU 205 determines whether or not the suggestion isperformed for all types into which respective pieces of the scannedimage data determined not to be appropriate are classified. Here, sincea suggestion for the “photograph” into which each piece of the scannedimage data of the sixth sheet to the tenth sheet of the scanned imagedata group is classified is not performed, the process returns to stepS1605, and a suggestion of a recommended setting item for the“photograph” is performed. FIG. 17B illustrates an example of thesuggestion screen 1706 at this time.

Note that subsequent processes are the same as those in step S1606 tostep S1610 described above, the CPU 205 makes the suggestion for alltypes in this manner, and terminates this processing.

As described above, according to the third exemplary embodiment, also inthe case where the plural pieces of paper documents are scanned, theload to change the setting of the image processing for the input imagedata can be reduced. Furthermore, in a case where the user has poorknowledge of the image processing as well, it is possible to providescanned image data desired by the user. Further it is possible toprovide a printed matter using the scanned image data.

Fourth Exemplary Embodiment

In a fourth exemplary embodiment, another configuration in a case whereplural pieces of paper documents are scanned will be described. Notethat in the fourth exemplary embodiment, processing for generatingscanned image data will be described, but a case where print image dataat the time of copy operation is generated can also be executed with thesame configuration. Note that the hardware configuration and the like ofthe MFP 101 according to the fourth exemplary embodiment is the same asthat of the first exemplary embodiment described above, and thus thedescriptions thereof will be omitted.

FIG. 18 is a flowchart for explaining processing of scanning the pluralpieces of paper documents and obtaining scanned image data, in the MFP101 according to the fourth exemplary embodiment. Note that theprocessing described in this flowchart is achieved by the CPU 205deploying the control program stored in the storage 211 to the RAM 206and executing the program. Note that in FIG. 18, processes common tothose of FIG. 6 and FIG. 16 described above are denoted by the samereference numbers, and descriptions thereof will be omitted.

In step S1603, in a case where the CPU 205 determines that the settinginput by the user is not appropriate, the process proceeds to stepS1801. In step S1801, the CPU 205 displays, on the console unit 203, asuggestion whether or not to execute image processing with therecommended setting based on the correspondence table 1001 representingthe classification result of the learned model and the recommendedsetting corresponding thereto illustrated in FIG. 10A.

FIG. 19 is a diagram illustrating an example of a suggestion screen 1901to be presented to the user in the MFP 101 according to the fourthexemplary embodiment.

For instance, it is a case where ten sheets of paper documents arescanned, the first sheet to the fifth sheet are each classified into adocument image and the sixth sheet to the tenth sheet are eachclassified into a photograph in step S1602, and setting change input bythe user is “character/photograph”, the console unit 203 displays thesuggestion screen 1901 in FIG. 19 in step S1801.

Here, reference numerals 1902 and 1903 each indicate a page displayportion, and as a result of classification with the learned model ofFIG. 8, the portion displays on which sheet the scanned image group thathas been determined to have the same type is located for each type. Inthe fourth exemplary embodiment, since the first sheet to the fifthsheet have the same type and the sixth sheet to the tenth sheet have thesame type, display as indicated by the reference numerals 1902 and 1903is performed. Furthermore, recommended setting items 1904 and 1905 aredisplayed based on the result of classifying the scanned image data withthe learned model in FIG. 8 and the correspondence table 1001 in FIG.10A. In the fourth exemplary embodiment, since the first sheet to thefifth sheet have each been classified into a “document image”, and thesixth sheet to the tenth sheet have each been classified into a“photograph”, display as in the recommended setting item 1904 and therecommended setting item 1905 is respectively performed.

Next, the process proceeds to step S1802, the CPU 205 determines whetheror not a “perform file generation with recommended setting” button 1906is pressed by the user, and if so, the process proceeds to step S1803.In step S1803, the CPU 205 executes image processing for all the scannedimage data based on the recommended setting item. The details of thisprocessing are similar to the processing described in step S610described above, and thus descriptions thereof will be omitted.

On the other hand, in step S1802, in a case where the CPU 205 determinesthat a “perform file generation with input setting” button 1907 ispressed, the process proceeds to step S1804. In step S1804, the CPU 205updates the correspondence table 1001 in FIG. 10A based on the settingitem input by the user and the classification group when the scannedimage group is classified with the learned model. For instance, in thesuggestion screen 1901 described above, in a case where the “performfile generation with input setting” button 1907 is pressed, therecommended setting item corresponding to the “document image” in thecorrespondence table 1001 and the recommended setting item correspondingto “photograph” in the correspondence table 1001 are updated. Thisupdate process is the same process as that in step S611 of the firstexemplary embodiment, and thus descriptions thereof will be omitted.

Next, the process proceeds to step S1805, in the same manner as in stepS1604, the CPU 205 causes the data processing unit 215 to executegeneration of scanned image data based on the setting item input by theuser through the console unit 203. In the fourth exemplary embodiment,on all the scanned image data of the scanned image data group, based onthe setting item input by the user, the generation of scanned image datais performed. The details of this processing are similar to theprocessing described in step S1604, and thus descriptions thereof willbe omitted.

As described above, according to the fourth exemplary embodiment, alsoin the case where the plural pieces of paper documents are scanned, theload to change the setting of the image processing for the input imagedata can be reduced. That is, it is possible to perform the processingwith the same number of operations as in the case where one paperdocument is scanned as assumed in the first exemplary embodiment, and itis possible to reduce the setting change load on the user. Furthermore,in a case where the user has poor knowledge of the image processing aswell, it is possible to provide scanned image data desired by the user.Further it is possible to provide a printed matter using the scannedimage data.

Other Embodiments

Embodiments of the present invention can also be realized by a computerof a system or apparatus that reads out and executes computer executableinstructions (e.g., one or more programs) recorded on a storage medium(which may also be referred to more fully as a ‘non-transitorycomputer-readable storage medium’) to perform the functions of one ormore of the above-described embodiments and/or that includes one or morecircuits (e.g., application specific integrated circuit (ASIC)) forperforming the functions of one or more of the above-describedembodiments, and by a method performed by the computer of the system orapparatus by, for example, reading out and executing the computerexecutable instructions from the storage medium to perform the functionsof one or more of the above-described embodiments and/or controlling theone or more circuits to perform the functions of one or more of theabove-described embodiments. The computer may comprise one or moreprocessors (e.g., central processing unit (CPU), micro processing unit(MPU)) and may include a network of separate computers or separateprocessors to read out and execute the computer executable instructions.The computer executable instructions may be provided to the computer,for example, from a network or the storage medium. The storage mediummay include, for example, one or more of a hard disk, a random-accessmemory (RAM), a read only memory (ROM), a storage of distributedcomputing systems, an optical disk (such as a compact disc (CD), digitalversatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, amemory card, and the like.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2019-236969, filed Dec. 26, 2019, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An image processing apparatus comprising: atleast one processor and at least one memory configured to function as: aholding unit that holds a type of image data and a setting item of imageprocessing corresponding to the type in association with each other; anoperation unit that is operable to cause a user to input the settingitem; an obtaining unit that obtains, by applying a learned learningmodel to image data which are input, a result of classifying the imagedata into the type; and a presentation unit that presents, based on thesetting item input by the operation unit and the setting item thatcorresponds to the type of the image data obtained by the obtaining unitand is held by the holding unit, a recommended setting itemcorresponding to the image data to the user.
 2. The image processingapparatus according to claim 1, wherein the presentation unit presents,in a case where the setting item input by the operation unit and thesetting item that corresponds to the type of the image data and is heldby the holding unit are different from each other, a setting item heldin the holding means as the recommend setting item.
 3. The imageprocessing apparatus according to claim 2, wherein the at least oneprocessor and the at least one memory configured to further function as:an updating unit that updates, in a case where, for presentation of therecommended setting item by the presentation unit, the user selects thesetting item input by the operation unit, contents related to therecommended setting item held in the holding unit with the setting iteminput by the operation unit.
 4. The image processing apparatus accordingto claim 3, wherein the updating unit updates, in a case where thenumber of times the user selects the setting item input by the operationunit becomes a predetermined number of times, contents related to therecommended setting item held in the holding unit with the setting iteminput by the operation unit.
 5. The image processing apparatus accordingto claim 2, wherein the presentation unit further displays an image in acase where the image data is processed based on the recommended settingitem corresponding to a type of the image data.
 6. The image processingapparatus according to claim 1, wherein the presentation unit presents,in a case where the image data includes plural types of image data, therecommended setting item corresponding to each of the types of the imagedata obtained by the obtaining unit.
 7. The image processing apparatusaccording to claim 1, wherein the presentation unit displays, in a casewhere the image data includes plural types of image data, pages ofimages in which the types of the image data obtained by the obtainingunit match with each other.
 8. The image processing apparatus accordingto claim 7, wherein the presentation unit further displays a page of animage to which the recommended setting item is applied.
 9. The imageprocessing apparatus according to claim 1, wherein the image data isscanned image data obtained by reading a paper document with a scanner,and the image processing includes processing for the scanned image data.10. The image processing apparatus according to claim 1, wherein theimage data is image data for copy, and the image processing includesprocessing for print image data to be used in the copy.
 11. A method ofcontrolling an image processing apparatus which holds a type of imagedata and a setting item of image processing corresponding to the type inassociation with each other, the method comprising: causing a user toinput the setting item; obtaining, by applying a learned learning modelto image data which is input, a result of classifying the image datainto the type; and presenting, based on the setting item that is inputand the setting item that corresponds to the type of the image data thatis obtained, and that is held, a recommended setting item correspondingto the image data to the user.
 12. A non-transitory computer-readablestorage medium storing a program for causing a processor to execute amethod of controlling an image processing apparatus which holds a typeof image data and a setting item of image processing corresponding tothe type in association with each other, the method comprising: causinga user to input the setting item; obtaining, by applying a learnedlearning model to image data which is input, a result of classifying theimage data into the type; and presenting, based on the setting item thatis input and the setting item that corresponds to the type of the imagedata that is obtained, and that is held, a recommended setting itemcorresponding to the image data to the user.